Face recognition technology (FRT) (also referred to as face tracking) is a vital part of a broad area of pattern recognition. Face tracking in general, and the tracking of moving people in natural scenes in particular, require a basic set of visual tasks to be performed robustly. This face tracking process typically includes three tasks, i.e., acquisition, normalization and recognition. The term acquisition refers to the detection and tracking of face-like image patches in a dynamic scene and localizing the face region from these patches. Normalization includes the alignment and normalization of the face images. Recognition is the representation and modeling of face images as identities, which can include the association of novel face images with known models.
Face tracking has involved two main approaches, i.e., a feature geometrical approach and a pictorial approach. The geometrical approach uses a spatial configuration of the facial features. The pictorial approach uses templates of the facial features.
A more recent approach is referred to as a deformable template approach that combines the elements of both pictorial and feature geometry approaches and has been applied to faces with varying pose and expression.
Being able to track a face from images contributes toward an ability to monitor a user's attention and reactions automatically and without intrusion, and has benefits in human-machine interaction.